Engineering Manager, AI Compute Platform (CaaS / GPUaaS)

Location: Dallas, TX (Relocation available)

Type: Direct Hire

• Competitive base salary + performance bonus
• 100% company-paid benefits

Overview

We are seeking an Engineering Manager, AI Compute Platform (CaaS / GPUaaS) to lead the design, scaling, and operational excellence of a next-generation compute platform delivering GPU-as-a-Service (GPUaaS) and Container-as-a-Service (CaaS) capabilities.

This platform serves as the backbone for large-scale AI/ML training, LLM workloads, and HPC applications, enabling customers to consume high-performance, GPU-accelerated infrastructure in a flexible, multi-tenant model across distributed data center environments.

You will lead a team responsible for building a bare-metal Kubernetes platform optimized for GPU workloads, ensuring high availability, performance, and efficient resource utilization at scale. This role sits at the intersection of Kubernetes, GPU infrastructure, HPC, and cloud-like service delivery models.

This is a hands-on leadership role focused on platform architecture, performance engineering, and automation, with direct impact on how GPU compute is delivered as a scalable service.

Key Responsibilities

Leadership & Team Development

Lead, mentor, and grow a team of engineers building next-generation AI compute platforms
Foster a culture of ownership, reliability, and continuous improvement
Drive alignment across platform, infrastructure, and product teams

Platform Architecture – GPUaaS / CaaS

Architect and scale a bare-metal Kubernetes platform delivering GPUaaS and CaaS capabilities
Design multi-tenant compute environments with strong isolation, scheduling, and resource governance
Define service models for GPU consumption, including workload orchestration, tenancy, and quota management

GPU Platform & Workload Optimization

Optimize GPU scheduling, sharing, and utilization across large-scale clusters (MIG, device plugins, scheduler extensions)
Support AI/ML training, LLM workloads, and HPC use cases with high throughput and low latency
Ensure efficient workload placement across hybrid orchestration models (Kubernetes + HPC schedulers)

Automation, SRE & Platform Operations

Drive Infrastructure-as-Code (Terraform, Ansible) and GitOps-based CI/CD practices
Implement SRE principles across observability, reliability, and incident response
Build automated workflows for cluster provisioning, scaling, and lifecycle management

Performance, Reliability & Capacity Planning

Own platform performance across thousands of GPU/CPU nodes
Define and track KPIs for utilization, latency, throughput, and system health
Lead capacity planning aligned with rapid AI compute demand growth

Cross-Functional & Ecosystem Collaboration

Partner with storage and networking teams to integrate distributed filesystems and high-speed interconnects (InfiniBand, RoCE)
Collaborate with hardware and software vendors (e.g., NVIDIA ecosystem) to optimize platform capabilities
Align platform architecture with evolving AI infrastructure and GPUaaS service offerings

Required Experience

7+ years of experience in platform engineering, infrastructure engineering, or SRE, with 2+ years in leadership
Proven experience building or operating Kubernetes platforms for GPU-intensive workloads (AI/ML or HPC)
Experience designing or supporting CaaS, GPUaaS, or multi-tenant compute platforms
Deep understanding of GPU scheduling, workload orchestration, and resource isolation
Strong expertise in Linux systems, networking, and performance engineering on bare-metal infrastructure
Experience managing large-scale, distributed compute environments
Strong experience with automation tools (Terraform, Ansible) and observability stacks (Prometheus, Grafana, Loki)
Excellent leadership and communication skills

Preferred Experience

Experience with NVIDIA ecosystem (GPU Operator, DCGM, MIG, device plugins)
Familiarity with HPC schedulers (Slurm, Flux, Volcano) and hybrid orchestration models
Experience with container runtimes (containerd, CRI-O) and Kubernetes internals
Contributions to open-source Kubernetes, AI infrastructure, or HPC platforms
Experience operating in hyperscale or AI-first infrastructure environments

Why This Role

Direct ownership of a GPUaaS / CaaS platform at scale
Work at the forefront of AI infrastructure and high-performance compute
Opportunity to define how GPU compute is delivered as a service in next-generation environments
High visibility role with impact across platform, product, and customer experience

Engineering Manager, HPC Kubernetes Platform